1. Field of the Invention
The invention relates generally to a method and system for the computerized analysis of radiographic images, and more specifically, to the determination of the likelihood of malignancy in pulmonary nodules using artificial neural networks (ANNs).
The present invention also generally relates to computerized techniques for automated analysis of digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; 5,732,697; 5,740,268; 5,790,690; 5,832,103; 5,873,824; 5,881,124; 5,931,780; 5,974,165; 5,982,915; 5,984,870; 5,987,345; 6,011,862; 6,058,322; 6,067,373; 6,075,878; 6,078,680; 6,088,473; 6,112,112; 6,138,045; 6,141,437; 6,185,320; 6,205,348 as well as U.S. patent application Ser. Nos. 08/173,935; 08/398,307 (PCT Publication WO 96/27846); 08/536,149; 08/900,188; 08/900,189; 09/027,468; 09/028,518; 09/092,004; 09/121,719; 09/141,535; 09/471,088; 09/692,218; 09/716,335; 09/759,333; 09/760,854; and 09/773,636; PCT patent applications PCT/US99/24007; PCT/US99/25998; PCT/US00/41299; PCT/US01/00680; PCT/US01/01478 and PCT/US01/01479 and U.S. provisional patent application Nos. 60/193,072 and 60/207,401, all of which are incorporated herein by reference.
The present invention includes use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the following LIST OF REFERENCES by the author(s) and year of publication and cross-referenced throughout the specification by reference to the respective number, in parentheses, of the reference:
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The entire contents of each related patent and application listed above and each reference listed in the LIST OF REFERENCES, are incorporated herein by reference.
Discussion of the Background
The differential diagnosis of pulmonary nodules on chest images is a difficult task for radiologists. Malignancy accounts for only 20% of all solitary pulmonary nodules on chest images (see Reference 1); however, most patients have been examined by computed tomography (CT) for a definite diagnosis. (See Reference 2) If radiologists could confirm confidently that many nodules are benign based on chest images, some unnecessary CT examinations would be avoided.
As disclosed in the above-cross-referenced International application No. PCT/US99/25998, in an effort to determine whether a nodule was benign or not, the outline of a nodule was drawn manually by radiologists. Various objective features were determined by use of the outline, and the likelihood of malignancy was determined by use of artificial neural networks (ANNs). Receiver operating characteristic (ROC) analysis indicated an encouraging result, that the Az value of the ANN output was greater than the average Az value obtained by radiologists in distinguishing between benign and malignant nodules. However, if a manual process were required for radiologists to draw the nodule outline, the practicality for utilizing the computer output as a second opinion to assist radiologists"" image interpretation would be limited.
Accordingly, an object of this invention is to provide a new and improved automated computerized method and system for implementing a computer-aided diagnostic (CAD) technique to assist radiologists in distinguishing benign and malignant lung nodules.
Another object of this invention is to provide a new and improved automated computerized method and system for the analysis of the likelihood of malignancy in solitary pulmonary nodules on chest images, wherein manual identification of nodules is avoided or simplified.
Another object of this invention is to provide a new and improved method and system for the analysis of the likelihood of malignancy in solitary pulmonary nodules using image classifiers including a linear discriminate analyzer and artificial neural networks.
A further object of this invention is to provide a new and improved method and system for the analysis and determination of the likelihood of malignancy in solitary pulmonary nodules whereby it is possible to reduce the number of follow-up CT imaging ordered by radiologists.
Another object of this invention is to provide a computer program product including a storage medium storing a novel program for performing the steps of the method.
These and other objects are achieved according to the invention by providing (1) a new and improved method for analyzing a nodule, (2) computer readable medium storing computer instructions for analyzing a nodule, and (3) a system for analyzing a nodule. The method, on which the computer instructions and the system of the present invention are based, includes obtaining a digital chest image in which a location of a nodule is identified; generating a difference image from chest image; identifying image intensity contour lines representative of respective image intensities in a region of interest including the nodule; and segmenting the nodule based on the image intensity contours to obtain an outline of the nodule.
Upon obtaining an outline of the nodule, the method further includes generating objective measures corresponding to physical features of the outline of the nodule; applying the generated objective measures to at least one classier, which may be a linear discriminant analyzer and/or an artificial neural network (ANN); and determining a likelihood of malignancy of the nodule based on an output of the at least one classifier.
According to another aspect of the present invention, there is provided a novel automated computerized method, computer readable medium storing computer instructions for analyzing a nodule, and system for the analysis of the likelihood of malignancy in solitary pulmonary nodules on chest images, wherein the location of a nodule in a chest radiograph is first manually indicated in a chest image, and a difference image including the identified nodule is produced by use of filters and then represented in a polar coordinate system. The nodule is then segmented automatically by analysis of contour lines of the gray-level distribution based on the polar-coordinate representation.
Once the nodule is segmented, clinical parameters (age and sex) and plural image features determined from the outline or texture analysis for inside and outside regions of the segmented nodule are subjected to linear discriminant analysis (LDA). A combination of selected plural features is evaluated as input to an artificial neural networks (ANN). The results of classification by the LDA and ANN establish thresholds defining whether a nodule is benign or not.